






import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


__a:     Dict   					=					logging.getLogger(__name__)
class 		SCREAMING_SNAKE_CASE__	(  __lowerCAmelCase     ):

  '''simple docstring'''







  def __init__(      self		:			List[str]       ,				lowerCamelCase		:			Union[str, Any]=-1							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    label_idx


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Union[Split, str]							)						->	List[str]:


         """simple docstring"""


         if isinstance(lowerCamelCase_       ,				lowerCamelCase_							):
                _UpperCAmelCase							=    mode.value
         _UpperCAmelCase							=    os.path.join(lowerCamelCase_       ,				f"""{mode}.txt"""							)
         _UpperCAmelCase							=    1
         _UpperCAmelCase							=    []
         with open(lowerCamelCase_       ,				encoding="""utf-8"""							) as f:
                _UpperCAmelCase							=    []
                _UpperCAmelCase							=    []
                for line in f:
                       if line.startswith("""-DOCSTART-"""							) or line == "" or line == "\n":
                              if words:
                                     examples.append(InputExample(guid=f"""{mode}-{guid_index}"""       ,				words=lowerCamelCase_       ,				labels=lowerCamelCase_							)							)
                                     guid_index += 1
                                     _UpperCAmelCase							=    []
                                     _UpperCAmelCase							=    []
                       else:
                              _UpperCAmelCase							=    line.split(""" """							)
                              words.append(splits[0]							)
                              if len(lowerCamelCase_							) > 1:
                                     labels.append(splits[self.label_idx].replace("""\n"""       ,				""""""							)							)
                              else:
                                     # Examples could have no label for mode = "test"
                                     labels.append("""O"""							)
                if words:
                       examples.append(InputExample(guid=f"""{mode}-{guid_index}"""       ,				words=lowerCamelCase_       ,				labels=lowerCamelCase_							)							)
         return examples


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			TextIO       ,				lowerCamelCase		:			TextIO       ,				lowerCamelCase		:			List							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    0
         for line in test_input_reader:
                if line.startswith("""-DOCSTART-"""							) or line == "" or line == "\n":
                       writer.write(lowerCamelCase_							)
                       if not preds_list[example_id]:
                              example_id += 1
                elif preds_list[example_id]:
                       _UpperCAmelCase							=    line.split()[0] + """ """ + preds_list[example_id].pop(0							) + """\n"""
                       writer.write(lowerCamelCase_							)
                else:
                       logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""       ,				line.split()[0]							)





  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			str							)						->	str:


         """simple docstring"""


         if path:
                with open(lowerCamelCase_       ,				"""r"""							) as f:
                       _UpperCAmelCase							=    f.read().splitlines()
                if "O" not in labels:
                       _UpperCAmelCase							=    ["""O"""] + labels
                return labels
         else:
                return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]



class 		SCREAMING_SNAKE_CASE__	(  __lowerCAmelCase     ):

  '''simple docstring'''







  def __init__(      self		:			List[str]							)						->	str:


         """simple docstring"""
         super().__init__(label_idx=-2							)





  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			str							)						->	str:


         """simple docstring"""


         if path:
                with open(lowerCamelCase_       ,				"""r"""							) as f:
                       _UpperCAmelCase							=    f.read().splitlines()
                if "O" not in labels:
                       _UpperCAmelCase							=    ["""O"""] + labels
                return labels
         else:
                return [
                    "O",
                    "B-ADVP",
                    "B-INTJ",
                    "B-LST",
                    "B-PRT",
                    "B-NP",
                    "B-SBAR",
                    "B-VP",
                    "B-ADJP",
                    "B-CONJP",
                    "B-PP",
                    "I-ADVP",
                    "I-INTJ",
                    "I-LST",
                    "I-PRT",
                    "I-NP",
                    "I-SBAR",
                    "I-VP",
                    "I-ADJP",
                    "I-CONJP",
                    "I-PP",
                ]



class 		SCREAMING_SNAKE_CASE__	(  __lowerCAmelCase     ):

  '''simple docstring'''







  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Union[Split, str]							)						->	Dict:


         """simple docstring"""


         if isinstance(lowerCamelCase_       ,				lowerCamelCase_							):
                _UpperCAmelCase							=    mode.value
         _UpperCAmelCase							=    os.path.join(lowerCamelCase_       ,				f"""{mode}.txt"""							)
         _UpperCAmelCase							=    1
         _UpperCAmelCase							=    []

         with open(lowerCamelCase_       ,				encoding="""utf-8"""							) as f:
                for sentence in parse_incr(lowerCamelCase_							):
                       _UpperCAmelCase							=    []
                       _UpperCAmelCase							=    []
                       for token in sentence:
                              words.append(token["""form"""]							)
                              labels.append(token["""upos"""]							)
                       assert len(lowerCamelCase_							) == len(lowerCamelCase_							)
                       if words:
                              examples.append(InputExample(guid=f"""{mode}-{guid_index}"""       ,				words=lowerCamelCase_       ,				labels=lowerCamelCase_							)							)
                              guid_index += 1
         return examples


  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			TextIO       ,				lowerCamelCase		:			TextIO       ,				lowerCamelCase		:			List							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    0
         for sentence in parse_incr(lowerCamelCase_							):
                _UpperCAmelCase							=    preds_list[example_id]
                _UpperCAmelCase							=    """"""
                for token in sentence:
                       out += f"""{token["form"]} ({token["upos"]}|{s_p.pop(0							)}) """
                out += "\n"
                writer.write(lowerCamelCase_							)
                example_id += 1





  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			str							)						->	Optional[int]:


         """simple docstring"""


         if path:
                with open(lowerCamelCase_       ,				"""r"""							) as f:
                       return f.read().splitlines()
         else:
                return [
                    "ADJ",
                    "ADP",
                    "ADV",
                    "AUX",
                    "CCONJ",
                    "DET",
                    "INTJ",
                    "NOUN",
                    "NUM",
                    "PART",
                    "PRON",
                    "PROPN",
                    "PUNCT",
                    "SCONJ",
                    "SYM",
                    "VERB",
                    "X",
                ]
import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
_SCREAMING_SNAKE_CASE 		=      logging.getLogger()

_SCREAMING_SNAKE_CASE 		=      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    os.makedirs(lowerCamelCase_   ,   exist_ok=lowerCamelCase_				)
    UpperCamelCase			  =      {"""source""": """What is love ?""", """target""": """life"""}
    UpperCamelCase			  =      {"""train""": 12, """val""": 2, """test""": 2}
    for split in ["train", "test", "val"]:
     for field in ["source", "target"]:
      UpperCamelCase			  =      """\n""".join([contents[field]] * n_lines[split]				)
      with open(os.path.join(lowerCamelCase_   ,   f"""{split}.{field}"""				)   ,   """w"""				) as f:
       f.write(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						str = "pytorch"				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_auto_remove_tmp_dir()
    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """output"""				)
    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """data"""				)
    self._create_dummy_data(data_dir=lowerCamelCase_				)

    UpperCamelCase			  =      f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

    if gpus > 0:
     testargs.append(f"""--gpus={gpus}"""				)
     if is_apex_available():
      testargs.append("""--fp16"""				)
    else:
     testargs.append("""--gpus=0"""				)
     testargs.append("""--distributed_backend=ddp_cpu"""				)
     testargs.append("""--num_processes=2"""				)

    UpperCamelCase			  =      [sys.executable, str(Path(finetune_rag.__file__				).resolve()				)] + testargs
    execute_subprocess_async(lowerCamelCase_   ,   env=self.get_env()				)

    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """metrics.json"""				)
    with open(lowerCamelCase_				) as f:
     UpperCamelCase			  =      json.load(lowerCamelCase_				)
    return result







   @require_torch_gpu
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_multi_gpu
   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=2				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_gpu
   @require_ray
   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1   ,   distributed_retriever="""ray"""				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_multi_gpu
   @require_ray
   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1   ,   distributed_retriever="""ray"""				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)
